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2.
Aust J Gen Pract ; 51(9): 696-702, 2022 09.
Article in English | MEDLINE | ID: covidwho-2026512

ABSTRACT

BACKGROUND AND OBJECTIVES: There is growing evidence regarding the effectiveness of registrar training through video cameras, which has relevance for quality supervision during times of crises such as the global COVID-19 pandemic. METHOD: Interviews were conducted in 2012 with supervisors, registrars and patients evaluating video camera use for tele-supervision across six rural sites in Gippsland, Australia. Thematic analysis was employed in 2013 - and re-examined in 2021 in light of the global COVID-19 pandemic - to explore user experience with video technology. RESULTS: Participants identified advantages of video supervision addressing distance and temporal issues, also emphasising quality supervision and education. Challenges included patient confidentiality, internet stability and loss of serendipitous 'corridor conversations'. DISCUSSION: Remote supervision is no longer simply an issue for rural and remote training. During crises such as a global pandemic, tele-supervision becomes the purview of all. There are distinct merits and limitations in adopting video technology, warranting consideration of individual training contexts. These findings can help inform remote supervision via video in varied milieu.


Subject(s)
COVID-19 , General Practice , Rural Health Services , Family Practice , General Practice/education , Humans , Pandemics/prevention & control
3.
Baruch, Joaquin, Rojek, Amanda, Kartsonaki, Christiana, Vijayaraghavan, Bharath K. T.; Gonçalves, Bronner P.; Pritchard, Mark G.; Merson, Laura, Dunning, Jake, Hall, Matthew, Sigfrid, Louise, Citarella, Barbara W.; Murthy, Srinivas, Yeabah, Trokon O.; Olliaro, Piero, Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Acharya, Subhash, Acker, Andrew, Adam, Elisabeth, Adrião, Diana, Al Ageel, Saleh, Ahmed, Shakeel, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Al Qasim, Eman, Alalqam, Razi, Alberti, Angela, Al‐dabbous, Tala, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Al‐Fares, Abdulrahman, Alfoudri, Huda, Ali, Imran, Ali, Adam, Shah, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, Rita, Alves, João Melo, Amaral, Maria, Amira, Nur, Ampaw, Phoebe, Andini, Roberto, Andréjak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean‐Benoît, Arora, Lovkesh, Arora, Rakesh, Artaud‐Macari, Elise, Aryal, Diptesh, Asensio, Angel, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W.; Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre‐Jensen, Roar, Baig, Nadia, Baillie, J. Kenneth, Baird, J. Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani‐Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S.; Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Bashir, Mustehan, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Benkerrou, Dehbia, Bennett, Suzanne, Bento, Luís, Berdal, Jan‐Erik, Bergeaud, Delphine, Bergin, Hazel, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Humaid, Felwa Bin, Kamarudin, Mohd Nazlin Bin, Bissuel, François, Bitker, Laurent, Bitton, Jonathan, Blanco‐Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne‐Hélène, Bolze, Pierre‐Adrien, Bompart, François, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho‐Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Boureau, Anne‐Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer‐Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Patrick, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Shaunagh, Browne, Alex, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse‐Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G.; 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McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A.; McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear‐Passard, Cécile, Mechlin, Maggie, Meher, Maqsood, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif, Mendes, Joao Joao, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J.; Mercier, Noémie, Mercier, Emmanuelle, Merckx, Antoine, Mergeay‐Fabre, Mayka, Mergler, Blake, Merson, Laura, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M.; Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miranda‐Maldonado, Hugo, Misnan, Nor Arisah, Mohamed, Tahira Jamal, Mohamed, Nik Nur Eliza, Moin, Asma, Molina, David, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Shona C.; Moore, Sarah, Cely, Lina Morales, Moro, Lucia, Morton, Ben, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Muneeb, Syed, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murphy, Lorna, Murphy, Aisling, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Muvindi, Himasha, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Mohd‐Hanafiah, Farah Nadia, Nagpal, Dave, Nagrebetsky, Alex, Narasimhan, Mangala, Narayanan, Nageswaran, Khan, Rashid Nasim, Nazerali‐Maitland, Alasdair, Neant, Nadège, Neb, Holger, Nekliudov, Nikita, Nelwan, Erni, Neto, Raul, Neumann, Emily, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair, Nitayavardhana, Prompak, Nonas, Stephanie, Noordin, Nurul Amani Mohd, Noret, Marion, Norharizam, Nurul Faten Izzati, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nowicka, Karolina, Nowinski, Adam, Nseir, Saad, Nunez, Jose I.; Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O.; Callaghan, Annmarie O.; O'Callaghan, Annmarie, Occhipinti, Giovanna, Oconnor, Derbrenn, O'Donnell, Max, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak‐Hyuk, O'Halloran, Sophie, O'Hearn, Katie, Ohshimo, Shinichiro, Oldakowska, Agnieszka, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L.; Ong, Jee Yan, Ong, David S. Y.; Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco‐Chamorro, Claudia Milena, Ortoleva, Jamel, Osatnik, Javier, O'Shea, Linda, O'Sullivan, Miriam, Othman, Siti Zubaidah, Ouamara, Nadia, Ouissa, Rachida, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Mario, Palacios, Amanda, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parke, Rachael, Parker, Melissa, Parra, Briseida, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Mical, Paul, Christelle, Paulos, Jorge, Paxton, William A.; Payen, Jean‐François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J.; Peelman, Florent, Peiffer‐Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D.; Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrovic, Michele, Petrov‐Sanchez, Ventzislava, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pitaloka, Ayodhia, Pius, Riinu, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska‐Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra‐Maris, Postma, Douwe F.; Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Preau, Sébastien, Prebensen, Christian, Preiser, Jean‐Charles, Prinssen, Anton, Pritchard, Mark G.; Priyadarshani, Gamage Dona Dilanthi, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Pulicken, Mathew, Purcell, Gregory, Quesada, Luisa, Quinones‐Cardona, Vilmaris, González, Víctor Quirós, Quist‐Paulsen, Else, Quraishi, Mohammed, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Rahardjani, Mutia, Rahman, Rozanah Abd, Rahman, Ahmad Kashfi Haji Ab, Rahutullah, Arsalan, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, Resseguier, Anne‐Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Luis Felipe, Reyes, Tiago, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Ridzuan, Siti Nurul Atikah Ahmad, Riera, Jordi, Rios, Ana L.; Rishu, Asgar, Rispal, Patrick, Risso, Karine, Nuñez, Maria Angelica Rivera, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L.; Robineau, Olivier, Roche‐Campo, Ferran, Rodari, Paola, Rodeia, Simão, Abreu, Julia Rodriguez, Roessler, Bernhard, Roger, Pierre‐Marie, Roger, Claire, Roilides, Emmanuel, Rojek, Amanda, Romaru, Juliette, Roncon‐Albuquerque, Roberto, Roriz, Mélanie, Rosa‐Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Roslan, Nurul Hidayah Mohammad, Rossanese, Andrea, Rossetti, Matteo, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D.; Ryan, Maria, Ryan, Maeve, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Saint‐Gilles, Maximilien, Sakiyalak, Pranya, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Sales, Gabriele, Sallaberry, Stéphane, Salmon Gandonniere, Charlotte, Salvator, Hélène, Sanchez, Olivier, Sanchez‐Miralles, Angel, Sancho‐Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre‐François, Sandulescu, Oana, Santos, Marlene, Sarfo‐Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E.; Sarton, Benjamine, Satya, Ankana, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Savvidou, Parthena, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Schwartz, Gary, Scott, Janet T.; Scott‐Brown, James, Sedillot, Nicholas, Seitz, Tamara, Selvanayagam, Jaganathan, Selvarajoo, Mageswari, Semaille, Caroline, Semple, Malcolm G.; Senian, Rasidah Bt, Senneville, Eric, Sequeira, Filipa, Sequeira, Tânia, Neto, Ary Serpa, Balazote, Pablo Serrano, Shadowitz, Ellen, Shahidan, Syamin Asyraf, Shamsah, Mohammad, Shankar, Anuraj, Sharjeel, Shaikh, Sharma, Pratima, Shaw, Catherine A.; Shaw, Victoria, Sheharyar, Ashraf, Shetty, Rohan, Shetty, Rajesh Mohan, Shi, Haixia, Shiekh, Mohiuddin, Shime, Nobuaki, Shimizu, Keiki, Shrapnel, Sally, Shrestha, Pramesh Sundar, Shrestha, Shubha Kalyan, Shum, Hoi Ping, Mohammed, Nassima Si, Siang, Ng Yong, Sibiude, Jeanne, Siddiqui, Atif, Sigfrid, Louise, Sillaots, Piret, Silva, Catarina, Silva, Rogério, Silva, Maria Joao, Heng, Benedict Sim Lim, Sin, Wai Ching, Sinatti, Dario, Singh, Punam, Singh, Budha Charan, Sitompul, Pompini Agustina, Sivam, Karisha, Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, Smyth, Michelle, Snacken, Morgane, So, Dominic, Soh, Tze Vee, Solberg, Lene Bergendal, Solomon, Joshua, Solomon, Tom, Somers, Emily, Sommet, Agnès, Song, Rima, Song, Myung Jin, Song, Tae, Chia, Jack Song, Sonntagbauer, Michael, Soom, Azlan Mat, Søraas, Arne, Søraas, Camilla Lund, Sotto, Alberto, Soum, Edouard, Sousa, Marta, Sousa, Ana Chora, Uva, Maria Sousa, Souza‐Dantas, Vicente, Sperry, Alexandra, Spinuzza, Elisabetta, Darshana, B. P. Sanka Ruwan Sri, Sriskandan, Shiranee, Stabler, Sarah, Staudinger, Thomas, Stecher, Stephanie‐Susanne, Steinsvik, Trude, Stienstra, Ymkje, Stiksrud, Birgitte, Stolz, Eva, Stone, Amy, Streinu‐Cercel, Adrian, Streinu‐Cercel, Anca, Stuart, David, Stuart, Ami, Subekti, Decy, Suen, Gabriel, Suen, Jacky Y.; Sultana, Asfia, Summers, Charlotte, Supic, Dubravka, Suppiah, Deepashankari, Surovcová, Magdalena, Suwarti, Suwarti, Svistunov, Andrey, Syahrin, Sarah, Syrigos, Konstantinos, Sztajnbok, Jaques, Szuldrzynski, Konstanty, Tabrizi, Shirin, Taccone, Fabio S.; Tagherset, Lysa, Taib, Shahdattul Mawarni, Talarek, Ewa, Taleb, Sara, Talsma, Jelmer, Tamisier, Renaud, Tampubolon, Maria Lawrensia, Tan, Kim Keat, Tan, Yan Chyi, Tanaka, Taku, Tanaka, Hiroyuki, Taniguchi, Hayato, Taqdees, Huda, Taqi, Arshad, Tardivon, Coralie, Tattevin, Pierre, Taufik, M. Azhari, Tawfik, Hassan, Tedder, Richard S.; Tee, Tze Yuan, Teixeira, João, Tejada, Sofia, Tellier, Marie‐Capucine, Teoh, Sze Kye, Teotonio, Vanessa, Téoulé, François, Terpstra, Pleun, Terrier, Olivier, Terzi, Nicolas, Tessier‐Grenier, Hubert, Tey, Adrian, Thabit, Alif Adlan Mohd, Thakur, Anand, Tham, Zhang Duan, Thangavelu, Suvintheran, Thibault, Vincent, Thiberville, Simon‐Djamel, Thill, Benoît, Thirumanickam, Jananee, Thompson, Shaun, Thomson, Emma C.; Thurai, Surain Raaj Thanga, Thwaites, Ryan S.; Tierney, Paul, Tieroshyn, Vadim, Timashev, Peter S.; Timsit, Jean‐François, Vijayaraghavan, Bharath Kumar Tirupakuzhi, Tissot, Noémie, Toh, Jordan Zhien Yang, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torres, Margarida, Torres, Antoni, Santos‐Olmo, Rosario Maria Torres, Torres‐Zevallos, Hernando, Towers, Michael, Trapani, Tony, Treoux, Théo, Tromeur, Cécile, Trontzas, Ioannis, Trouillon, Tiffany, Truong, Jeanne, Tual, Christelle, Tubiana, Sarah, Tuite, Helen, Turmel, Jean‐Marie, Turtle, Lance C. W.; Tveita, Anders, Twardowski, Pawel, Uchiyama, Makoto, Udayanga, P. G. Ishara, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad.
Influenza and Other Respiratory Viruses ; 2022.
Article in English | Web of Science | ID: covidwho-2019369

ABSTRACT

Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded. Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe arid Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years;geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did riot meet the case definition, the CFR increased. Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

4.
The British journal of surgery ; 108(Suppl 9), 2021.
Article in English | EuropePMC | ID: covidwho-1999521

ABSTRACT

Background In the UK around 50% of cases of pancreatitis are caused by gallstones. BSG guidelines recommend ERCP is undertaken within 72h of onset of pain and patients should undergo definitive treatment with cholecystectomy if fit enough during the index admission or within two weeks of discharge to avoid the risk of potentially fatal recurrent pancreatitis. A national audit in 2015 showed that 34.2% of patients receive definitive treatment. During the first COVID-19 wave our surgical service was forced to modify practice including more conservative/non operative management potentially increasing the possibility of recurrent pancreatitis and thus complications. Methods We performed a retrospective audit of patients presenting to our unit with gallstone pancreatitis during the first wave of the COVID-19 pandemic from March to August 2020 (COVID) and compared this to the same period in 2019 (pre-COVID). Patients were filtered from a larger dataset of all admissions with an ICD-10 coding of any biliary disease. Patient demographics, admission details, investigations, surgical management and post-operative complications were recorded. This was then audited against the standards in the BSG guidelines for the management of pancreatitis. ResultsP-EGS30 TablePre-COVIDCOVIDPresentations with GS pancreatitis5264Unique patients5056No. of repeat presentations in study period27Median age (years)6569Median length of stay44.5ERCP [ERCP < 72 hours]5 [2] (40%)11 [6] (54%)Average time to ERCP (days)3.83.5Cholecystectomy37 (74%)32 (57%)Hot procedures < 2 weeks[CT1] 30 (60%)17 (30%) p = 0.002†BSG guidance fulfilled33 (77%)27 (47%)   p < 0.001†Complications37 * Patients with severe pancreatitis and those unfit for intervention excluded. † χ2 test. Conclusions There were significant differences in the management of the groups. Most significantly in the number of hot procedures and number of patients receiving definitive treatment, a consequence of the conservative approach during COVID. Our pre-COVID results are similar to our previous audit in 2016;76% received definitive treatment. Those that didn’t have definitive treatment were generally due to frailty/co-morbidities. Majority of ERCP delays were due to weekend effect. Of the 40 patients who didn’t receive definitive treatment 16 have represented with biliary flares/pancreatitis in the year following the study period highlighting the importance of definitive treatment.

5.
AUANews ; 27(4):89-90, 2022.
Article in English | Academic Search Complete | ID: covidwho-1812664
6.
J Am Heart Assoc ; 11(9): e024207, 2022 05 03.
Article in English | MEDLINE | ID: covidwho-1807754

ABSTRACT

Background Ongoing exercise intolerance of unclear cause following COVID-19 infection is well recognized but poorly understood. We investigated exercise capacity in patients previously hospitalized with COVID-19 with and without self-reported exercise intolerance using magnetic resonance-augmented cardiopulmonary exercise testing. Methods and Results Sixty subjects were enrolled in this single-center prospective observational case-control study, split into 3 equally sized groups: 2 groups of age-, sex-, and comorbidity-matched previously hospitalized patients following COVID-19 without clearly identifiable postviral complications and with either self-reported reduced (COVIDreduced) or fully recovered (COVIDnormal) exercise capacity; a group of age- and sex-matched healthy controls. The COVIDreducedgroup had the lowest peak workload (79W [Interquartile range (IQR), 65-100] versus controls 104W [IQR, 86-148]; P=0.01) and shortest exercise duration (13.3±2.8 minutes versus controls 16.6±3.5 minutes; P=0.008), with no differences in these parameters between COVIDnormal patients and controls. The COVIDreduced group had: (1) the lowest peak indexed oxygen uptake (14.9 mL/minper kg [IQR, 13.1-16.2]) versus controls (22.3 mL/min per kg [IQR, 16.9-27.6]; P=0.003) and COVIDnormal patients (19.1 mL/min per kg [IQR, 15.4-23.7]; P=0.04); (2) the lowest peak indexed cardiac output (4.7±1.2 L/min per m2) versus controls (6.0±1.2 L/min per m2; P=0.004) and COVIDnormal patients (5.7±1.5 L/min per m2; P=0.02), associated with lower indexed stroke volume (SVi:COVIDreduced 39±10 mL/min per m2 versus COVIDnormal 43±7 mL/min per m2 versus controls 48±10 mL/min per m2; P=0.02). There were no differences in peak tissue oxygen extraction or biventricular ejection fractions between groups. There were no associations between COVID-19 illness severity and peak magnetic resonance-augmented cardiopulmonary exercise testing metrics. Peak indexed oxygen uptake, indexed cardiac output, and indexed stroke volume all correlated with duration from discharge to magnetic resonance-augmented cardiopulmonary exercise testing (P<0.05). Conclusions Magnetic resonance-augmented cardiopulmonary exercise testing suggests failure to augment stroke volume as a potential mechanism of exercise intolerance in previously hospitalized patients with COVID-19. This is unrelated to disease severity and, reassuringly, improves with time from acute illness.


Subject(s)
COVID-19 , Heart Failure , Case-Control Studies , Exercise Test/methods , Exercise Tolerance , Humans , Magnetic Resonance Spectroscopy , Oxygen , Oxygen Consumption , Stroke Volume
7.
Semin Thorac Cardiovasc Surg ; 2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1768935

ABSTRACT

The COVID-19 pandemic significantly affected health care and in particular surgical volume. However, no data surrounding lost hospital revenue due to decreased cardiac surgical volume have been reported. The National Inpatient Sample database was used with decreases in cardiac surgery at a single center to generate a national estimate of decreased cardiac operative volume. Hospital charges and provided charge to cost ratios were used to create estimates of lost hospital revenue, adjusted for 2020 dollars. The COVID period was defined as January to May of 2020. A Gompertz function was used to model cardiac volume growth to pre-COVID levels. Single center cardiac case demographics were internally compared during January to May for 2019 and 2020 to create an estimate of volume reduction due to COVID. The maximum decrease in cardiac surgical volume was 28.3%. Cumulative case volume and hospital revenue loss during the COVID months as well as the recovery period totaled over 35 thousand cases and 2.5 billion dollars. Institutionally, patients during COVID months were younger, more frequently undergoing a CABG procedure, and had a longer length of stay. The pandemic caused a significant decrease in cardiac surgical volume and a subsequent decrease in hospital revenue. This data can be used to address the accumulated surgical backlog and programmatic changes for future occurrences.

8.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326516

ABSTRACT

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure -- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

9.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324171

ABSTRACT

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

10.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-307366

ABSTRACT

The economic disruption from the COVID-19 pandemic prompted governments around the world to initiate an unprecedented number of temporary lending and tax deferment programs. Which firms will benefit from these programs? What are the implications for firm balance sheets and post-crisis survival? We provide some novel insights on these questions by studying one of the first government programs of this type, which Sweden launched at the height of the 2008-2009 financial crisis. The Swedish program allowed firms to temporarily suspend payment of all labor-related taxes and fees, treating these deferred amounts as a short-term loan from the government. Firms participating in the program are younger, less profitable, hold fewer cash reserves, are more leveraged, and have less unused slack in their credit lines when the crisis hits. Given the structure of the Swedish program, it provided more liquidity to firms with relatively larger ex ante wage bills. Exploiting this feature of the policy, we find that firms use the program to increase overall debt levels rather than to substitute for other borrowing. Firms use the funds to avoid making even deeper cuts to current assets. Despite the increase in leverage, access to the lending program is unrelated to the likelihood a firm files for bankruptcy and is negatively related to the likelihood a firm encounters severe financial distress in the years immediately following the crisis.

11.
Global Networks ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-1642652

ABSTRACT

This paper evaluates ways in which labour issues in global value chains for medical gloves have been affected by, and addressed through, the COVID‐19 pandemic. It focuses on production in Malaysia and supply to the United Kingdom's National Health Service and draws on a large‐scale survey with workers and interviews with UK government officials, suppliers and buyers. Adopting a Global Value Chain (GVC) framework, the paper shows how forced labour endemic in the sector was exacerbated during the pandemic in the context of increased demand for gloves. Attempts at remediation are shown to operate through both a reconfigured value chain in which power shifted dramatically to the manufacturers and a context where public procurement became higher in profile than ever before. It is argued that the purchasing power of governments must be leveraged in ways that more meaningfully address labour issues, and that this must be part of value chain resilience. [ FROM AUTHOR] Copyright of Global Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

12.
Front Cardiovasc Med ; 8: 764599, 2021.
Article in English | MEDLINE | ID: covidwho-1598692

ABSTRACT

Background: Acute myocardial damage is common in severe COVID-19. Post-mortem studies have implicated microvascular thrombosis, with cardiovascular magnetic resonance (CMR) demonstrating a high prevalence of myocardial infarction and myocarditis-like scar. The microcirculatory sequelae are incompletely characterized. Perfusion CMR can quantify the stress myocardial blood flow (MBF) and identify its association with infarction and myocarditis. Objectives: To determine the impact of the severe hospitalized COVID-19 on global and regional myocardial perfusion in recovered patients. Methods: A case-control study of previously hospitalized, troponin-positive COVID-19 patients was undertaken. The results were compared with a propensity-matched, pre-COVID chest pain cohort (referred for clinical CMR; angiography subsequently demonstrating unobstructed coronary arteries) and 27 healthy volunteers (HV). The analysis used visual assessment for the regional perfusion defects and AI-based segmentation to derive the global and regional stress and rest MBF. Results: Ninety recovered post-COVID patients {median age 64 [interquartile range (IQR) 54-71] years, 83% male, 44% requiring the intensive care unit (ICU)} underwent adenosine-stress perfusion CMR at a median of 61 (IQR 29-146) days post-discharge. The mean left ventricular ejection fraction (LVEF) was 67 ± 10%; 10 (11%) with impaired LVEF. Fifty patients (56%) had late gadolinium enhancement (LGE); 15 (17%) had infarct-pattern, 31 (34%) had non-ischemic, and 4 (4.4%) had mixed pattern LGE. Thirty-two patients (36%) had adenosine-induced regional perfusion defects, 26 out of 32 with at least one segment without prior infarction. The global stress MBF in post-COVID patients was similar to the age-, sex- and co-morbidities of the matched controls (2.53 ± 0.77 vs. 2.52 ± 0.79 ml/g/min, p = 0.10), though lower than HV (3.00 ± 0.76 ml/g/min, p< 0.01). Conclusions: After severe hospitalized COVID-19 infection, patients who attended clinical ischemia testing had little evidence of significant microvascular disease at 2 months post-discharge. The high prevalence of regional inducible ischemia and/or infarction (nearly 40%) may suggest that occult coronary disease is an important putative mechanism for troponin elevation in this cohort. This should be considered hypothesis-generating for future studies which combine ischemia and anatomical assessment.

13.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296774

ABSTRACT

Mortality rates during the COVID-19 pandemic have varied by orders of magnitude across communities in the United States. Individual, socioeconomic, and environmental factors have been linked to health outcomes of COVID-19. It is now widely appreciated that the environmental microbiome, composed of microbial communities associated with soil, water, atmosphere, and the built environment, impacts immune system development and susceptibility to immune-mediated disease. The human microbiome has been linked to individual COVID-19 disease outcomes, but there are limited data on the influence of the environmental microbiome on geographic variation in COVID-19 across populations. To fill this knowledge gap, we used taxonomic profiles of fungal communities associated with 1,135 homes in 494 counties from across the United States in a machine learning analysis to predict COVID-19 Infection Fatality Ratios (the number of deaths caused by COVID-19 per 1000 SARS-CoV-2 infections;'IFR'). Here we show that exposure to increased fungal diversity, and in particular indoor exposure to outdoor fungi, is associated with reduced SARS-CoV-2 IFR. Further, we identify seven fungal genera that are the predominant drivers of this protective signal and may play a role in suppressing COVID-19 mortality. This relationship is strongest in counties where human populations have remained stable over at least the previous decade, consistent with the importance of early-life microbial exposures. We also assessed the explanatory power of 754 other environmental and socioeconomic factors, and found that indoor-outdoor fungal beta-diversity is amongst the strongest predictors of county-level IFR, on par with the most important known COVID-19 risk factors, including age. We anticipate that our study will be a starting point for further integration of environmental mycobiome data with population health information, providing an important missing link in our capacity to identify vulnerable populations. Ultimately, our identification of specific genera predicted to be protective against COVID-19 mortality may point toward novel, proactive therapeutic approaches to infectious disease.

14.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295236

ABSTRACT

Background: During a pandemic, estimates of geographic variability in disease burden are important but limited by the availability and quality of data. Methods: We propose a framework for estimating geographic variability in testing effort, total number of infections, and infection fatality ratio (IFR). Because symptomatic people are more likely to seek testing, we use a noncentral hypergeometric model that accounts for differential probability of positive tests. We apply this framework to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs from March 1, 2020 to October 31, 2020. Using data on population size, number of observed cases, number of reported deaths in each U.S. county and state, and number of tests in each U.S. state, we develop a series of estimators to identify the number of SARS-CoV-2 infections and IFRs at the county level. We then perform a simulation and compare the estimated values to simulated values to demonstrate the validity of our approach. Findings: Applying the county-level estimators to the real, unsimulated COVID-19 data spanning March 1, 2020 to October 31, 2020 from across the U.S., we found that IFRs varied from 0 to 0.0273, with an interquartile range of 0.0022 and a median of 0.0018. The estimators for IFRs, number of infections, and number of tests showed high accuracy and precision;for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.88, 0.95, and 0.74, respectively. Interpretation: We propose an estimation framework that can be used to identify area-level variation in IFRs and performs well to estimate county-level IFRs in the U.S. COVID-19 pandemic.

15.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-293527

ABSTRACT

Background: During a pandemic, estimates of geographic variability in disease burden are important but limited by the availability and quality of data. Methods: We propose a framework for estimating geographic variability in testing effort, total number of infections, and infection fatality ratio (IFR). Because symptomatic people are more likely to seek testing, we use a noncentral hypergeometric model that accounts for differential probability of positive tests. We apply this framework to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs from March 1, 2020 to October 31, 2020. Using data on population size, number of observed cases, number of reported deaths in each U.S. county and state, and number of tests in each U.S. state, we develop a series of estimators to identify the number of SARS-CoV-2 infections and IFRs at the county level. We then perform a simulation and compare the estimated values to simulated values to demonstrate the validity of our approach. Findings: Applying the county-level estimators to the real, unsimulated COVID-19 data spanning March 1, 2020 to October 31, 2020 from across the U.S., we found that IFRs varied from 0 to 0.0273, with an interquartile range of 0.0022 and a median of 0.0018. The estimators for IFRs, number of infections, and number of tests showed high accuracy and precision;for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.88, 0.95, and 0.74, respectively. Interpretation: We propose an estimation framework that can be used to identify area-level variation in IFRs and performs well to estimate county-level IFRs in the U.S. COVID-19 pandemic.

16.
Front Glob Womens Health ; 2: 649104, 2021.
Article in English | MEDLINE | ID: covidwho-1533667

ABSTRACT

Background: Lockdown measures have been enforced globally in response to the COVID-19 pandemic. Given the comorbidity burden in women with polycystic ovary syndrome (PCOS), these lockdown measures may have a particularly negative impact on sleep health, quality of life (QoL), and depression/stress levels in this population. The aim of this study was to explore whether such potential problems were present in women with PCOS during the COVID-19 lockdown in the UK. Methods: UK women with PCOS were recruited through social media into a cross-sectional study during the COVID-19 lockdown. The study survey was delivered online, and included demographic and COVID-19 relevant questions, as well as validated questionnaires/scales, namely the Insomnia Severity Index (ISI), Depression Anxiety and Stress Scale (DASS-21), and PCOSQOL questionnaire. Results: Three hundred and thirty-three women with PCOS [median age: 30.0 (9.0) years] were recruited. Participants were dichotomized based on responses regarding the impact of COVID-19 restrictions on their sleep [negative (N = 242) vs. no/positive (N = 91) impact]. No differences were noted between groups regarding age, time since PCOS diagnosis, body mass index, or number of comorbidities. Based on the ISI, 44.2% of participants reporting a negative impact on sleep exhibited at least moderately severe clinical insomnia. Compared to those who reported no/positive effect on sleep, the participants reporting a negative impact on sleep also reported poorer QoL, based on the total PCOSQOL score, with a greater impact of PCOS and poorer mood in the corresponding PCOSQOL domains. Based on the DASS-21, the latter also had statistically higher depression and stress levels compared to the former. Finally, for this cohort significant inverse correlations were noted between the ISI and PCOSQOL scores (total and domain scores), whilst the DASS-21 and ISI scores were positively correlated (all p-values <0.001). Conclusion: The majority of recruited UK women with PCOS reported that the COVID-19 lockdown had a negative impact on their sleep, which was also associated with impaired QoL and higher depression/stress levels. Whilst further research is required, women with PCOS should be considered a vulnerable population that may experience an adverse impact on sleep, QoL and mental health well-being due to lockdown measures during the COVID-19 pandemic.

17.
Journal of Corporate Finance ; : 102116, 2021.
Article in English | ScienceDirect | ID: covidwho-1474708

ABSTRACT

The economic disruption from the COVID-19 pandemic prompted governments around the world to initiate an unprecedented number of temporary lending and tax deferment programs. Which firms will benefit from these programs? What are the implications for firm balance sheets and post-crisis survival? We provide some novel insights on these questions by studying one of the first government programs of this type, which Sweden launched at the height of the 2008–2009 financial crisis. The Swedish program allowed firms to temporarily suspend payment of all labor-related taxes and fees, treating these deferred amounts as a short-term loan from the government. Firms participating in the program are younger, less profitable, hold fewer cash reserves, are more leveraged, and have less unused slack in their credit lines when the crisis hits. Given the structure of the Swedish program, it provided more liquidity to firms with relatively larger ex ante wage bills. Exploiting this feature of the policy, we find that firms use the program to increase overall debt levels rather than to substitute for other borrowing. The leverage increase is due entirely to higher levels of non-bank debt. Firms use the funds to avoid making even deeper cuts to current assets. Despite the increase in leverage, access to the lending program is unrelated to the likelihood a firm files for bankruptcy and is negatively related to the likelihood a firm encounters severe financial distress in the years immediately following the crisis.

18.
J Phys Chem B ; 125(44): 12166-12176, 2021 11 11.
Article in English | MEDLINE | ID: covidwho-1475246

ABSTRACT

The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.


Subject(s)
COVID-19 , Deep Learning , Pharmaceutical Preparations , Antiviral Agents/pharmacology , Drug Design , Humans , Molecular Docking Simulation , SARS-CoV-2
19.
Ear Nose Throat J ; : 1455613211049004, 2021 Sep 29.
Article in English | MEDLINE | ID: covidwho-1443724

ABSTRACT

Rhinosporidiosis, an infectious granulomatous disease, is seldom encountered in the United States. We present a case of rhinosporidiosis in a 26-year-old man, who presented with an unusual mass in his nasal cavity. Suspicion for rhinosporidiosis was high due to the patient's travel and activity history. After imaging and proper diagnosis, surgery was performed to excise the lesion. As international travel resumes during the COVID-19 pandemic, the potential for encountering this rare organism is heightened.

20.
Postdigital Science and Education ; 2021.
Article in English | PMC | ID: covidwho-1351417
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